Systems and methods to quantify risk associated with suppliers or geographic locations

ABSTRACT

Computer-implemented systems and methods to quantify risk associated with suppliers or geographic locations at which suppliers or global internal delivery centers are located. The systems and methods transform risk parameter data into risk metrics that allow comparison of relative risk between suppliers, supplier sites, or geographic locations, and allow comparison of risk metrics to minimum risk scores calculated for a given metric. The systems and methods further provide guidance/proposed action to take based on the generated risk metrics.

I. BACKGROUND

The present invention is directed to computer-implemented systems andmethods for transforming data into risk metrics to quantify the riskassociated with a particular supplier or suppliers, or the potentialrisk associated with a geographic location at which a supplier or globalinternal delivery center is located.

Risk assessment and management is essential for success of a widevariety of endeavors, including individual and organizationalactivities. An organization may face risk in many forms, includingclient risks, competitor risks, supplier risks, legal risks,technological risks, political risks, and environmental risks. Properlyidentifying, understanding and evaluating risks can allow anorganization to prepare for and respond to events beyond its control.

For example, a company that offers goods for sale may purchase componentparts for its goods from one or more suppliers. When selecting asupplier, companies typically look to more than just price. For example,reliable and timely delivery of component parts and whether the supplieris a sustainable business are also key factors when selecting asupplier. Companies often depend on suppliers to timely supply rawmaterials or other inputs to ensure uninterrupted sales and distributionof goods and services. A disruption in the supply chain may reduce thecapability of a company to provide its goods and services, therebyreducing its sales and revenue. A disruption in the supply chain mayalso cause the company to breach contracts it has entered to sell itsgoods and service to customers, thereby subjecting the company to legalliability. A disruption in supply can therefore have severe consequencesfor a company. Identifying risks in the supply chain, includingparticular risks associated with suppliers, is critical to the ongoingsuccess of an organization.

A supplier may face disruptions in its business for reasons directlyrelating to the operations and business decisions of the supplier, orfor reasons wholly beyond the supplier's control. For example, asupplier that does not invest in training and development for its workforce may face a high attrition rate and a shortage of labor. As anotherexample, a supplier may depend on supplies or services from athird-party supplier, such as electric service from a government utilitycompany, to maintain its business operations. In a third example, aviable and successful company can face disruptions because oflocation-based events beyond its control such as natural disasters,geo-political events, or changes in laws. Any disruption in supply fromthe third-party supplier may cause disruptions further along the supplychain.

However, risks associated with suppliers have been difficult toquantify. Consequently, acquiring a comprehensive understanding of therisks face by companies can be challenging. In addition, processing andanalyzing the data in a timely manner is critical to taking necessaryactions in response to the data.

II. SUMMARY OF INVENTION

An object of the present invention is to provide a method for convertingdata from disparate sources into quantified risk metrics that can beused to assess the risk associated with purchasing goods or servicesfrom a particular supplier using analytics and algorithms to determinethe impact of specific events/parameters on risks.

An object of the present invention is to provide a method for convertingdata from disparate source into quantified risk metrics that can be usedto assess the risk associated with purchasing goods or services from aparticular location.

Yet another object of the present invention is to provide guidance basedon the risk metrics to help organizations choose a supplier or mitigatethe risk associated with a supplier, or to take advantage ofopportunities brought about by changes in a location's or supplier'srisk profile.

A further object of the present invention is to provide a system wherebythe calculation of risk metrics may be adjusted by altering thepercentage weights assigned to a risk category or risk sub-category.

Another object of the present invention is to provide a best score as abasis of comparison for the calculated risk metrics.

Another object of the present invention is to provide guidance onactions or steps to take to mitigate specific risks as they occur. Theguidance may be changed based on a change in the risk score.

III. BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the invention, reference is had to thefollowing description of the accompanying Figures. Like referencenumbers are used to refer to like and corresponding elements of thevarious Figures.

FIG. 1 is a chart showing the elements used in the transformation ofdata into risk categories and a composite risk score according to thepresent invention.

FIG. 2 is a flow chart depicting the disclosed process of transformingparameter values into risk categories and a composite risk score.

FIG. 3 is a sample dashboard of risk scores for a supplier.

FIG. 4 is a sample dashboard of risk scores for multiple suppliers.

FIG. 5 is a sample dashboard of risk scores for a location.

FIG. 6 is a sample dashboard of risk scores for multiple locations.

IV. DETAILED DESCRIPTION OF THE INVENTION

As described in detail hereinafter, the present invention is directed toa computer-based system and process for converting data into riskmetrics to quantify the potential risk associated with a particularsupplier or suppliers, or the potential risk associated with ageographic location at which a supplier is located.

The disclosed processes and functionalities can be implemented bysuitable computer-executable instructions. The computer-executableinstructions may be stored as software code components or modules on oneor more computer readable media, such as non-volatile memories, volatilememories, DASD arrays, magnetic tapes, floppy diskettes, hard drives,optical storage devices, etc. or any other appropriate computer-readablemedium or storage device.

Additionally, the functions of the disclosed embodiments may beimplemented on one computer or shared/distributed among two or morecomputers in or across a network. Communications between computersimplementing embodiments can be accomplished using any electronic,optical, radio frequency signals, or other suitable methods and tools ofcommunication in compliance with known network protocols. The systemutilizes existing computer hardware capabilities, and electroniccommunications links, for example, to receive and process information.

The system may include a computer server having electronic access to adatabase containing files or tables. The server can be electronicallycoupled to a global computer network such as, for example, the Internet.The server may communicate with at least one user interface, which caninclude conventional computer input, output and display devices. Theuser interface is preferably a remote computer interface capable ofconnection to the server via a network. The system may also beimplemented by other known methods of computer networking. The serverpreferably operates under control of computer software to carry out theinventive process steps described in greater detail hereinafter. Thecomputer software preferably includes XML, XSL, HTML, VML and JavaScriptcapability to prepare and present information in an Internet web browserformat.

A. Supplier Risk Metrics

FIG. 1 depicts an exemplary hierarchy of elements used to generate acomposite risk score (107) according to one embodiment of the presentinvention. As shown in FIG. 1, a composite risk score (107) may begenerated from multiple risk categories (105). Each category (105) maybe generated from multiple risk sub-categories (103). Each risksub-category may be generated from parameters (101). In one embodiment,hundreds of parameters (101) may be used.

If the present invention is used to evaluate the risk associated with aparticular supplier, exemplary risk categories (105) may include (1)“Financial Risk”; (2) “Service Maturity Risk”; (3) “Governance Risk”;(4) “People Risk”; (5) “Infrastructure Risk”; (6) “Client Risk”; (7)“Partner/Alliances Risk”; and (8) “Thought Leadership Risk.”

Each risk category (105) may be associated with at least one risksub-category (103). For example, the risk category (105) “FinancialRisk” may be associated with risk sub-categories (103) directed toinvestment, ratios, balance sheet, liquidity, profitability, revenue,and revenue diversity metrics. The risk category (105) “Service MaturityRisk” may be associated with risk sub-categories (103) directed to keytalent pool size, quality certifications, specialized certifications,operating model, service and vertical lines, and geographical metrics.The risk category (105) “Governance Risk” may be associated with risksub-categories (103) directed to strength of corporate governance,changes in organizational structure, functional, and operatinggovernance levels. The risk category (105) “People Risk” may beassociated with risk sub-categories (103) directed to attrition, newhires, productivity, utilization, and employee development and trainingmeasures. The risk category (105) “Infrastructure Risk” may beassociated with risk sub-categories (103) directed to physical security,information security, data theft/fraud, measures of delivery centernetworks, opening of new delivery centers and risks related to them,uptime policy/performance, and expansion plans. The risk category (105)“Client Risk” may be associated with risk sub-categories (103) directedto acquisition, retention/flight, concentration, and satisfaction. Therisk category (105) “Partner/Alliances Risk” may be associated with risksub-categories (103) directed to key partners and additions acrossenterprises, new alliances/partnerships during the relevant time period,and vertical and horizontal solutions. The risk category (105) “ThoughtLeadership Risk” may be associated with risk sub-categories (103)directed to innovation, published papers, event and topic leadership,patent information and key CSR metrics.

Each risk sub-category (103) may be associated with at least oneparameter (101). For example, “People Risk” category (105) may beassociated with an “attrition” sub-category (103) which may beassociated with parameters (101) relating to attrition, such as thepercentage of staff employees who departed the supplier during aparticular time period, and the percentage of executives who departedthe supplier during a particular time period.

FIG. 2 is a flow chart depicting how a composite risk score (107) for asupplier may be generated from parameters (101), sub-categories (103),and categories (105). At Step 202, parameter values are entered intomemory of a computer. A software application receives the parametervalues and stores them in memory. When the parameter values are enteredinto memory, they are associated with one of the parameters (101).

A graphical user interface may be used to facilitate entry of theparameter values. The interface may have fields that allow entry ofdata. The software or interface may require entry of particularparameter values in a certain format or range. For example, certainnumeric parameter values may be required as integers. A parameter (101)directed to the number of earthquakes experienced in a particular regionmay require an integer value. As another example, the interface mayrequire a percentage value entered between zero per centum and onehundred per centum. If a parameter (101) is directed to the percentageof staff employees who departed a supplier during a particular timeperiod, a parameter value entered for that parameter (101) would beentered as a percentage value between zero per centum and one hundredper centum. If a parameter (101) is directed to revenue earned by thesupplier during a particular time period, a parameter value entered forthat parameter (101) would be entered as a unit of currency. Thegraphical user interface may facilitate entry of parameter values byindicating the unit format for each parameter, or by requiring theparameter value to be entered in the required unit format before theparameter value is entered into memory.

At Step 204, a risk score is assigned to each parameter (101) based onthe parameter value entered for the parameter (101). For each parameter(101), a series of bands or ranges are assigned. The parameter valueentered for the parameter (101) would fall within one of the assignedbands. Each band may be correlated to a risk score for the parameter(101). The risk score assigned to a parameter (101) therefore depends onthe band in which the parameter value lies. For example, for a parameter(101) directed to the number of typhoons experienced in a region, a bandspanning the range of “one to three” may be correlated with a risk scoreof 2, a band spanning the range of “four to six” may be correlated witha risk score of 4, a band spanning the range of “seven to nine” may becorrelated with a risk score of 7, and a band spanning the range“greater than nine” may be correlated with a risk score of 9. If theactual number of typhoons experienced in a region is five, then a riskscore of 4 would be assigned to the parameter directed to the number oftyphoons.

The risk score assigned to the bands may be a number chosen from apredetermined range such as one to ten. The number assigned to the riskparameter (101) reflects the relative level of risk reflected by theparameter value. For example, a risk score in the range of 1.0 to 2.0may correspond to a negligible risk, a risk score in the range of 2.1 to4.0 may correspond to a low risk, a risk score in the range of 4.1-6.0may correspond to a moderate risk, a risk score in the range of 6.1 to8.0 may correspond to a high risk, and a risk score in the range of 8.1to 10.0 may correspond to an extreme risk.

For presentation purposes, a color may be associated with each riskscore range. For example, the color dark green may be associated withthe risk score range of 1.0 to 2.0, the color light green may beassociated with the risk score range of 2.1 to 4.0, the color yellow maybe associated with the risk score range of 4.1-6.0, the color light redmay be associated with the risk score range of 6.1 to 8.0, and the colordark red may be associated with the risk score range of 8.1 to 10.0.

At Step 206, a weight percentage is assigned to each parameter (101).The weight percentages determine the importance of each risk parameter(101) in the assessment of risk for the supplier. For example, althougha parameter value entered for a parameter (101) may be an extreme valuefor that parameter (101), the parameter (101) may not have a significantimpact on the risk assessment of a supplier. The weight percentageassigned to that parameter (101) would therefore be a relatively lowpercentage. The sum of weight percentages assigned to all parameters(101) associated with a particular risk sub-category (103) may total 100per centum. If no parameter values are entered for one or moreparameters (101), the weight percentages assigned to the parameters(101) may be redistributed to the parameters (101) for which values havebeen entered. The redistribution of weight percentages may be done byevenly dividing the redistributed percentages among the parameters (101)for which values were entered, or according to the relative weightsassigned to the parameters (101) for which values were entered.

At Step 208, a risk score is calculated for each risk sub-category (103)based on the values entered for the parameters (101) associated with therisk sub-category (103) and the weight percentages assigned to eachparameter (101). For example, the software may calculate the risk scorefor each risk sub-category (103) by tallying the sum of the product ofeach risk score assigned to a parameter (101) and the weight percentageassigned to the parameter (101).

The risk score may be constrained to a number within a certain rangesuch as one to ten. For example, a risk score in the range of 1.0 to 2.0may correspond to a negligible risk, a risk score in the range of 2.1 to4.0 may correspond to a low risk, a risk score in the range of 4.1-6.0may correspond to a moderate risk, a risk score in the range of 6.1 to8.0 may correspond to a high risk, and a risk score in the range of 8.1to 10.0 may correspond to an extreme risk. A color may also be assignedto each risk sub-category (103). The color assigned to each risksub-category (103) may follow the same or similar exemplary approachdescribed above for the colors associated with parameters (101).

At Step 210, weight percentages are assigned to each risk sub-category(103). The weight percentages determine the importance of eachsub-category (103) in the assessment of risk for the supplier. The sumof weight percentages assigned to all sub-categories (103) associatedwith a particular risk category (105) may total 100 per centum.

At Step 212, a risk score is calculated for each risk category (105)based on the risk scores of each sub-category (103) associated with therisk category (105) and the weight percentages assigned to eachsub-category (103). For example, the software may calculate the riskscore for each risk category (105) by tallying the sum of the product ofeach risk score assigned to a sub-category (103) and the weightpercentage assigned to the sub-category (103).

The risk score may be constrained to a number within a certain rangesuch as one to ten. For example, a risk score in the range of 1.0 to 2.0may correspond to a negligible risk, a risk score in the range of 2.1 to4.0 may correspond to a low risk, a risk score in the range of 4.1-6.0may correspond to a moderate risk, a risk score in the range of 6.1 to8.0 may correspond to a high risk, and a risk score in the range of 8.1to 10.0 may correspond to an extreme risk. A color may also be assignedto each risk category (105). The color assigned to each risk category(105) may follow the same or similar exemplary approach described abovefor the colors associated with parameters (101).

At Step 214, weight percentages are assigned to each risk category(105). The weight percentages determine the importance of each category(105) in the assessment of risk for the supplier. The sum of weightpercentages assigned to all categories (105) may total 100 per centum.Note that the weight percentages assigned to the risk parameters (101),the sub-categories (103) and the categories (105) do not necessarilyneed to be assigned in the order reflected in FIG. 2. The weightpercentages assigned to the risk parameters (101) may be assigned at anytime up until Step 208, the weight percentages assigned to the risksub-categories (103) may be assigned at any time up until Step 212, andthe weight percentages assigned to the risk categories (105) may beassigned at any time up until Step 216.

At Step 216, a composite risk score (107) is calculated for a supplierbased on the risk scores of each category (105) and the weightpercentages assigned to each category (105). For example, the softwaremay calculate a composite risk score (107) by tallying the sum of theproduct of each risk score assigned to a category (105) and the weightpercentage assigned to the category (105).

The risk score may be constrained to a number within a certain rangesuch as one to ten. For example, a risk score in the range of 1.0 to 2.0may correspond to a negligible risk, a risk score in the range of 2.1 to4.0 may correspond to a low risk, a risk score in the range of 4.1-6.0may correspond to a moderate risk, a risk score in the range of 6.1 to8.0 may correspond to a high risk, and a risk score in the range of 8.1to 10.0 may correspond to an extreme risk. A color may also be assignedto the composite risk score (107). The color assigned to the compositerisk score (107) may follow the same or similar exemplary approachdescribed above for the colors associated with parameters (101).

The above process may be performed for multiple suppliers. For eachsupplier, parameter values may be entered in memory and associated withparameters (101) at Step 202. Risk scores and weight percentages may beassigned to each parameter (101) at Steps 204 and 206, respectively.Risk scores may be generated for risk sub-categories (103) at Step 208and weight percentages may be assigned to the risk sub-categories (103)at Step 210. Risk scores may be generated for risk categories (105) atStep 212 and weight percentages may be assigned to the risk categories(105) at Step 214. A composite score for each supplier may be generatedat Step 216. However, each step need not be performed at the same timefor each supplier.

B. Location Risk Metrics

The above examples are directed to transforming data into graphical andquantitative risk evaluation metrics reflecting the potential riskassociated with a supplier or suppliers. The present invention may alsobe used to transform data into graphical and quantitative riskevaluation metrics reflecting the potential risk associated with ageographic location at which one or more suppliers are located. Thegeographic location may be, for example, a city or a country.

The process for evaluating risk associated with a geographic location issimilar to the process for risk associated with a supplier. The stepsand elements disclosed in FIGS. 1 and 2 would be the same for evaluatingrisk associated with a location, but the particular risk parameters(101), risk sub-categories (103), and risk categories (105) would bedirected to parameters and categories more relevant to analysis of alocation. Exemplary risk categories (105) may include (1)“Macro-Economic Risk”; (2) “Financial Risk”; (3) “Geo-Political Risk”;(4) “Infrastructure Risk”; (5) “Business Risk”; (6) “Legal Risk”; (7)“Scalability Risk”; and (8) “Quality of Life Risk.”

Each risk category (105) may be associated with at least one risksub-category (103). For example, the risk category (105) “Macro-EconomicRisk” may be associated with risk sub-categories (103) directed toinflation, foreign direct investment, credit risk, currency risk, andmarket fluctuations. The risk category (105) “Financial Risk” may beassociated with risk sub-categories (103) directed to labor cost metricsfor each of information technology outsourcing (ITO), business processoutsourcing (BPO), knowledge process outsourcing (KPO), as well asoperational costs, and taxation factors. The risk category (105)“Geo-Political Risk” may be associated with risk sub-categories (103)directed to political risk and stability, social and security risk (e.g.terrorism, prevalence of travel advisory/warnings), and natural disasterrisks. The risk category (105) “Infrastructure Risk” may be associatedwith risk sub-categories (103) directed to government support andincentives to source, power and utilities, transport and facilities,technological readiness, and quality of connectivity metrics. The riskcategory (105) “Business Risk” may be associated with risksub-categories (103) directed to ease of doing business, regulatory andstatutory requirements, business sophistication, and trade andlogistics. The risk category (105) “Legal Risk” may be associated withrisk sub-categories (103) directed to legal and regulatory policy andacts, cybercrime, ITO and BPO industry trade union activity, and keyin-market labor and workday laws. The risk category (105) “ScalabilityRisk” may be associated with risk sub-categories (103) directed to sizeand growth characteristics for ITO and BPO, provider and processmaturity, worker population spreads, attrition/hiring measures, andlanguages. The risk category (105) “Quality of Life Risk” may beassociated with risk sub-categories (103) directed to e.g. expat qualityof life measures and business support and amenities.

Each risk sub-category (103) may be associated with at least oneparameter (101). For example, “Financial Risk” category (105) may beassociated with an “operational cost” sub-category (103) which may beassociated with parameters (101) relating to rental growth, fuel pricesand the cost of registering property.

C. Supplier Site Risk Metrics

A supplier may have more than one geographic location from which itsupplies customers. For example, a supplier may provide components toits customers from sites in India and China. The present invention maybe used to compare risks associated with each of a supplier's locations.

The exemplary hierarchy of elements and processing steps depicted inFIGS. 1 and 2 may be used to generate a composite risk score (107) foreach supplier site. A composite risk score (107) may be generated frommultiple risk categories (105). Each risk category (105) may begenerated from multiple risk sub-categories (103). Each risksub-category may be generated from parameters (101). The parameters(101), risk sub-categories (103) and risk categories (105) may concernthe supplier, each supplier site, or the geographic location of eachsite.

D. Reporting Generated Risk Metrics

The parameter values and risk scores assigned to or generated forparameters (101), risk sub-categories (103), risk categories (105), andthe composite risk score (107) may be presented by a number of means andin a number of formats. For example, the risk scores may be presented asnumerical values which may be arranged in a table. The risk scores mayalso be presented as graphed data points, bar charts, pie graphs, or anyother graphical representation. The risk scores from more than one timeperiod may be presented concurrently.

FIG. 3 depicts a sample table of risk scores for a supplier for thesecond and third quarters of the year 2015 (“Q2 2015” and “Q3 2015,”respectively). Risk scores for each quarter are presented in separatecolumns. For each quarter, the table includes risk scores generated foreight risk categories and a composite risk score (i.e. “Total RiskScore”) for the supplier. Between each column may be symbols indicatingwhether the risk score for a category increased, decreased, or remainedthe same from one time period to the next. For example, an arrowpointing up may be used to indicate that the risk score for a categoryincreased between quarters, an arrow pointing down may be used toindicate that the risk score for a category decreased between quarters,and a horizontal line may be used to indicate that the risk score for acategory did not change between quarters.

The table also includes a best composite risk score among all evaluatedsuppliers, and a best risk score among all evaluated suppliers for eachrisk category. The best composite risk score may be presented for one ofthe presented time periods. For example, in FIG. 3, the best compositerisk score may correspond to Q3 2015. The best composite risk score mayalso be presented for a range of time periods or for all of the timeperiods for which data has been collected.

Risk scores for more than one supplier may be presented concurrently.FIG. 4 depicts a sample table of risk scores for multiple suppliers. Foreach supplier, risk scores for the second and third quarters of the year2015 (“Q2 2015” and “Q3 2015,” respectively) are presented in separatecolumns. Between each column are symbols indicating whether the riskscore for a category increased, decreased, or remained the same from onequarter to the next.

Risk scores for locations may be presented. The locations may be acountry, a city, or another region or geographic location. FIG. 5depicts a sample table of risk scores for a location for the second andthird quarters of the year 2015 (“Q2 2015” and “Q3 2015,” respectively).For each quarter, the table includes risk scores generated for eightrisk categories and a composite risk score (i.e. “Total Risk Score”) forthe location. Between each column are symbols indicating whether therisk score for a category increased, decreased, or remained the samefrom one time period to the next.

The table also includes a best composite risk score among all evaluatedlocations, and a best risk score among all evaluated locations for eachrisk category. The best composite risk score may be presented for one ofthe presented time periods. For example, in FIG. 5, the best compositerisk score may correspond to Q3 2015. The best composite risk score mayalso be presented for a range of time periods or for all of the timeperiods for which data has been collected.

Risk scores for more than one supplier may be presented concurrently.FIG. 6 depicts a sample table of risk scores for multiple locations. Foreach location, risk scores for the second and third quarters of the year2015 (“Q2 2015” and “Q3 2015,” respectively) are presented in separatecolumns. Between each column are symbols indicating whether the riskscore for a category increased, decreased, or remained the same from onequarter to the next.

Colors associated with each risk score may be reflected in thepresentation of the scores. In FIG. 3, the Total Risk Score for thesecond quarter of 2015 is 5.04. If, for example, the color yellow isassociated with the risk score range of 4.1-6.0, the background color ofthe table cell containing the Total Risk Score for the second quarter of2015 may be displayed as yellow. In the alternative, the font color ofthe risk score may correspond to the color associated with the riskscore, or color associated with the range of risk scores in which therisk score falls. Color may also be applied to the Best scores, shownfor example in the right-most column of the table depicted in FIG. 3.

The risk scores may be presented in a hard copy report. The risk scoresmay also be presented in electronic form, such as data on an Internetweb page or as a .csv file. If the risk scores are presented on anInternet web page, a user accessing the web page from a remote computermay request the elements underlying each of the risk scores presented.For example, a user viewing the table depicted in FIG. 3 may requestfurther detail concerning the “Governance Risk” category risk score forthe third quarter of 2015. The user would then be presented the risksub-categories associated with the “Governance Risk” category and therisk scores generated for each sub-category. Similarly if a userrequested further detail concerning a sub-category, the user would bepresented the parameters associated with the sub-category and the riskscores assigned to each parameter.

A user may also be allowed to customize the process for generating riskscores. For example, as shown in FIG. 2, the risk scores generated (208)for risk sub-categories are based in part on the weight percentagesassigned (206) to the risk parameters. Also, the risk scores generated(212) for risk categories are based in part on the weight percentagesassigned (210) to sub-categories. The composite risk scores are alsogenerated (216) based in part on weight percentages assigned (214) tothe risk categories. In one embodiment of the present invention, thesystem used to generate the risk scores may receive from a user of thesystem a request to change the weight percentages assigned to riskparameters, risk sub-categories, or risk categories. The system may thengenerate new risk scores for risk sub-categories or risk categories, orgenerate a new composite risk score based on the received weightpercentages.

Guidance or proposed action steps may also be presented to suggest howto address or mitigate risk associated with a supplier or a location.Guidance may also be given on a broader, long-term basis for all riskcategories. The guidance may also be given in response to specific risktrigger events such as natural disasters like earthquakes and tropicalstorms or on financial events like a quarterly financial report by asupplier.

For example, depending on the risk scores generated for a particularlocation, companies working with supplier in that location may beadvised to review their supplier's disaster recovery and businesscontinuity plans and programs, and ensure the supplier is equipped withemergency measures to deal with any hazard situation. Companies usingmultinational suppliers may be advised to include clauses in theircontract with the supplier that would allow moving work from one countryto another based on situations like increasing security concerns andnatural disasters.

The guidance to be presented may be determined based on a particularrisk score or based on a band of risk scores. For example, certainguidance may be presented if a risk score is above a specific value,while different guidance may be presented if a risk score is below aspecific value. Certain guidance may be presented if a risk score fallswithin a particular band or range. The guidance presented may be basedon whether a risk score changes from one band to another, or dependingon which risk score or scores change the most between time periods. Theguidance presented may be based on the weight percentage assigned to oneor more risk parameters (101), risk subcategories (103), or riskcategories (105). The guidance presented may be based on the change invalue of a parameter (101) risk score, a sub-category (103) risk score,or a category (105) risk score that causes the greatest changes in thecomposite risk score.

The system of the present invention may issue an alert based on datainput into the system relevant to one or more parameters (101). Forexample, if a natural disaster strikes a location, the system may issuea report describing the natural disaster. The report may further includeguidance or proposed action steps to taken in response to the naturaldisaster.

Other modifications to and variations of the invention will be apparentto those skilled in the art from the foregoing disclosure and teachings.Thus, while only certain embodiments of the invention have beenspecifically described herein, it will be apparent that numerousmodifications may be made thereto without departing from the spirit andscope of the invention, as defined in the appended claims.

1. A computer-implemented method for converting parameter values intorisk evaluation metrics and providing guidance based on the metrics, themethod comprising: Generating a number of database fields for receivingrisk parameter data wherein the number of database fields for receivingrisk parameter data is greater than 1; Generating a number of databasefields for receiving risk sub-category values wherein the number ofdatabase fields for receiving risk sub-category values is greater than1, and wherein each database field for receiving risk parameter data isassociated with a database field for receiving a risk sub-categoryvalue; Generating a number of database fields for receiving riskcategory values wherein the number of database fields for receiving riskcategory values is greater than 1, and wherein each database field forreceiving risk sub-category values is associated with a database fieldfor receiving a risk category value; Entering risk parameter data intodatabase fields of a graphical user interface, wherein the riskparameter data corresponds to a number of suppliers, wherein the numberof suppliers is greater than 1, and wherein at least one database fieldrequires entry of data in a specific format or range; Determining aparameter risk score based on the data entered into a database field forreceiving risk parameter data, wherein the parameter risk score is apredetermined number corresponding to the data; Assigning a weightpercentage to each database field for receiving risk parameter datawherein the weight percentage is chosen from a first set of weightpercentages; For each supplier, entering a sub-category risk score intoa database field for receiving a risk sub-category value based on atleast one parameter risk score and the weight percentages assigned toeach database field for receiving risk parameter data; Assigning to eachdatabase field for receiving a risk sub-category value a weightpercentage chosen from a second set of weight percentages; For eachsupplier, entering a category risk score into each database field forreceiving risk category values based on at least one sub-category riskscore and the weight percentages assigned to each database field forreceiving risk sub-category values; Assigning to each database field forreceiving a risk category value a weight percentage chosen from a thirdset of weight percentages; Generating a first composite risk score for asupplier based on at least one category risk score and the weightpercentages assigned to each database field for receiving a riskcategory value corresponding to that supplier; For one supplier,presenting to a display screen the first composite risk score, theentered category risk scores for that supplier, and a recommendationbased on the category risk score entered into a database field forreceiving risk category values; Presenting to the display screen thelowest category risk score among all category risk scores entered intodatabase field for receiving risk category values for all suppliers;Assigning a color to each category risk score, wherein said color isselected from a pre-selected set of colors, and wherein eachpre-selected color is associated with a risk score; For each categoryrisk score, presenting to the display screen the color assigned to thecategory risk score; and Issuing to the display screen an alert based ondata entered into one or more database fields for receiving riskparameter data.
 2. The method of claim 1 further comprising receiving afourth set of weight percentages; and generating a second composite riskscore for the supplier based on a category risk score and the weightpercentage assigned to each database field for receiving a risk categoryvalue from the fourth set of weight percentages.
 3. The method of claim1 wherein the alert includes guidance based on data entered into one ormore database fields.
 4. The method of claim 1 wherein said databasefields for receiving risk category values correspond to categoriesselected from the categories of financial risk, service maturity risk,governance risk, people risk, infrastructure risk, client risk,partner/alliances risk, and thought leadership risk.
 5. Acomputer-implemented method for converting parameter values into riskevaluation metrics and providing guidance based on the metrics, themethod comprising: Generating a number of database fields for receivingrisk parameter data wherein the number of database fields for receivingrisk parameter data is greater than 1; Generating a number of databasefields for receiving risk sub-category values wherein the number ofdatabase fields for receiving risk sub-category values is greater than1, and wherein each database field for receiving risk parameter data isassociated with a database field for receiving a risk sub-categoryvalue; Generating a number of database fields for receiving riskcategory values wherein the number of database fields for receiving riskcategory values is greater than 1, and wherein each database field forreceiving risk sub-category values is associated with a database fieldfor receiving a risk category value; Entering risk parameter data intodatabase fields of a graphical user interface, wherein the riskparameter data corresponds to a number of sites of a supplier, whereinthe number of sites is greater than 1, and wherein at least one databasefield requires entry of data in a specific format or range; Determininga parameter risk score based on the data entered into a database fieldfor receiving risk parameter data, wherein the parameter risk score is apredetermined number corresponding to the data; Assigning a weightpercentage to each database field for receiving risk parameter datawherein the weight percentage is chosen from a first set of weightpercentages; For each supplier site, entering a sub-category risk scoreinto a database field for receiving a risk sub-category value based onat least one parameter risk score and the weight percentages assigned toeach database field for receiving risk parameter data; Assigning to eachdatabase field for receiving a risk sub-category value a weightpercentage chosen from a second set of weight percentages; For eachsupplier site, entering a category risk score into each database fieldfor receiving risk category values based on at least one sub-categoryrisk score and the weight percentages assigned to each database fieldfor receiving risk sub-category values; Assigning to each database fieldfor receiving a risk category value a weight percentage chosen from athird set of weight percentages; Generating a first composite risk scorefor a supplier site based on at least one category risk score and theweight percentages assigned to each database field for receiving a riskcategory value corresponding to that supplier; For one supplier site,presenting to a display screen the first composite risk score, theentered category risk scores for that site, and a recommendation basedon the category risk score entered into a database field for receivingrisk category values; Presenting to the display screen the lowestcategory risk score among all category risk scores entered into databasefield for receiving risk category values for all supplier sites;Assigning a color to each category risk score, wherein said color isselected from a pre-selected set of colors, and wherein eachpre-selected color is associated with a risk score; For each categoryrisk score, presenting to the display screen the color assigned to thecategory risk score; and Issuing to the display screen an alert based ondata entered into one or more database fields for receiving riskparameter data.
 6. The method of claim 5 further comprising receiving afourth set of weight percentages; and generating a second composite riskscore for each supplier site based on a category risk score and theweight percentage assigned to each database field for receiving a riskcategory value from the fourth set of weight percentages.
 7. The methodof claim 5 wherein the alert includes guidance based on data enteredinto one or more database fields.
 8. The method of claim 5 wherein saiddatabase fields for receiving risk category values correspond tocategories selected from the categories of financial risk, servicematurity risk, governance risk, people risk, infrastructure risk, clientrisk, partner/alliances risk, and thought leadership risk.
 9. Anon-transitory computer readable storage medium on which is embedded oneor more computer programs, said one or more computer programsimplementing a method for converting parameter values into riskevaluation metrics and providing guidance based on the metrics, said oneor more computer programs comprising a set of instructions for: Storinga number of risk parameters wherein the number of risk parameters isgreater than 1; Associating each risk parameter with a risksub-category; Associating each risk sub-category with a risk category;Receiving parameter values into memory of a first device, said parametervalues corresponding to risk parameters for a number of supplierswherein the number of suppliers is greater than 1; Assigning a riskscore to each risk parameter based on the parameter value entered forthe risk parameter, wherein each risk score is a predetermined numbercorresponding to the parameter value; Assigning to each risk parameter aweight percentage from a first set of weight percentages; For eachsupplier, generating a risk score for each risk sub-category based onthe risk score and weight percentage assigned to each risk parameterassociated with the risk sub-category; Assigning to each risksub-category a weight percentage from a second set of weightpercentages; For each supplier, generating a risk score for each riskcategory based on the risk score and weight percentage assigned to eachrisk sub-category associated with the risk category; Assigning to eachrisk category a weight percentage from a third set of weightpercentages; Generating a first composite risk score for a supplierbased on the risk score and weight percentage assigned to each riskcategory corresponding to that supplier; For one supplier, presenting toa display screen the composite risk score, the risk score of each riskcategory for that supplier, and a recommendation based on the risk scoreof one risk category; Presenting to the display screen the risk scorereflecting the lowest risk calculated for each risk category among allof the suppliers; Assigning a color to each category risk score, whereinsaid color is selected from a pre-selected set of colors, and whereineach pre-selected color is associated with a risk score; For eachcategory risk score, presenting to the display screen the color assignedto the category risk score; and Issuing to the display screen an alertbased on data entered into one or more database fields for receivingrisk parameter data.
 10. The non-transitory computer readable storagemedium according to claim 9, said one or more computer programs furthercomprising a set of instructions for receiving a fourth set of weightpercentages; and generating a second composite risk score for thesupplier based on the risk score assigned to each risk category and theweight percentage assigned to each risk category from the fourth set ofweight percentages.
 11. The non-transitory computer readable storagemedium according to claim 9, wherein the alert includes guidance basedon data entered into one or more database fields.
 12. The non-transitorycomputer readable storage medium according to claim 9, wherein said riskcategories are selected from the categories of financial risk, servicematurity risk, governance risk, people risk, infrastructure risk, clientrisk, partner/alliances risk, and thought leadership risk.
 13. Anon-transitory computer readable storage medium on which is embedded oneor more computer programs, said one or more computer programsimplementing a method for converting parameter values into riskevaluation metrics and providing guidance based on the metrics, said oneor more computer programs comprising a set of instructions for: Storinga number of risk parameters wherein the number of risk parameters isgreater than 1; Associating each risk parameter with a risksub-category; Associating each risk sub-category with a risk category;Receiving parameter values into memory of a first device, said parametervalues corresponding to risk parameters for a number of sites of asupplier, wherein the number of sites is greater than 1; Assigning arisk score to each risk parameter based on the parameter value enteredfor the risk parameter, wherein each risk score is a predeterminednumber corresponding to the parameter value; Assigning to each riskparameter a weight percentage from a first set of weight percentages;For each supplier site, generating a risk score for each risksub-category based on the risk score and weight percentage assigned toeach risk parameter associated with the risk sub-category; Assigning toeach risk sub-category a weight percentage from a second set of weightpercentages; For each supplier site, generating a risk score for eachrisk category based on the risk score and weight percentage assigned toeach risk sub-category associated with the risk category; Assigning toeach risk category a weight percentage from a third set of weightpercentages; Generating a first composite risk score for a supplier sitebased on the risk score and weight percentage assigned to each riskcategory corresponding to that supplier; For one supplier site,presenting to a display screen the first composite risk score, the riskscore of each risk category for that supplier, and a recommendationbased on the risk score of one risk category; Presenting to the displayscreen the risk score reflecting the lowest risk calculated for eachrisk category among all of the supplier sites; Assigning a color to eachcategory risk score, wherein said color is selected from a pre-selectedset of colors, and wherein each pre-selected color is associated with arisk score; For each category risk score, presenting to the displayscreen the color assigned to the category risk score; and Issuing to thedisplay screen an alert based on data entered into one or more databasefields for receiving risk parameter data.
 14. The non-transitorycomputer readable storage medium according to claim 13, said one or morecomputer programs further comprising a set of instructions for receivinga fourth set of weight percentages; and generating a second compositerisk score for each supplier site based on the risk score assigned toeach risk category and the weight percentage assigned to each riskcategory from the fourth set of weight percentages.
 15. Thenon-transitory computer readable storage medium according to claim 13,wherein the alert includes guidance based on data entered into one ormore database fields.
 16. The non-transitory computer readable storagemedium according to claim 13, wherein said risk categories are selectedfrom the categories of financial risk, service maturity risk, governancerisk, people risk, infrastructure risk, client risk, partner/alliancesrisk, and thought leadership risk.
 17. The method of claim 1 furthercomprising redistributing the weight percentages assigned to thedatabase field for receiving risk parameter data if no risk parameterdata is entered into one or more database field of the graphical userinterface for receiving risk parameter data.
 18. The method of claim 5further comprising redistributing the weight percentages assigned to thedatabase field for receiving risk parameter data if no risk parameterdata is entered into one or more database field of the graphical userinterface for receiving risk parameter data.
 19. The method of claim 9further comprising redistributing the weight percentages assigned to therisk parameters if no parameter value is received for one or more riskparameters.
 20. The method of claim 13 further comprising redistributingthe weight percentages assigned to the risk parameters if no parametervalue is received for one or more risk parameters.